Introduction to R
for Social Scientists

Workshop Day 2B | 2022-07-26
Jeffrey M. Girard | Pitt Methods

Wrangle III

Summarize

Although we store data about many observations…
…we often want to summarize across observations
- This is like folding the tibble down to one row

We’ve seen functions that summarize vectors
- length(), sum(), min(), max()
- mean(), median(), sd(), var()

summarize() lets us use them on tibbles
- It works very similarly to mutate()
- It always creates a tibble as output

Summarize Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

sales <- 
  tibble(
    customer = c(1, 2, 3, 1, 3),
    store = c("A", "A", "A", "B", "B"),
    items = c(25, 20, 16, 10, 5),
    spent = c(685, 590, 392, 185, 123)
  ) |> 
  print()

# ==============================================================================

# USECASE: Summarize the typical sales

my_summary <- 
  sales |> 
  summarize(
    avg_items = mean(items),
    avg_spent = mean(spent)
  ) |> 
  print()

# ==============================================================================

# PITFALL: Don't use summary() instead of summarize()

my_summary <- 
  sales |> 
  summary(
    avg_items = mean(items),
    avg_spent = mean(spent)
  ) |> 
  print()

# ==============================================================================

# USECASE: Use more than one summary function

my_summary <- 
  sales |> 
  summarize(
    total_items = sum(items),
    total_spent = sum(spent),
    avg_items = mean(items),
    avg_spent = mean(spent)
  ) |> 
  print()

# ==============================================================================

# USECASE: Use counting functions

my_counts <- 
  sales |> 
  summarize(
    n_sales = n(),
    n_customers = n_distinct(customer),
    n_stores = n_distinct(store)
  ) |> 
  print()

Group Summarize

We can also summarize a tibble by group
- This is like folding the tibble multiple times
- Specifically, we fold down to one row per group

The syntax for summarize is identical
- The only difference is to the tibble
- We first pass it through group_by()
- Pipelines make this very easy

Group Summarize Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

sales <- 
  tibble(
    customer = c(1, 2, 3, 1, 3),
    store = c("A", "A", "A", "B", "B"),
    items = c(25, 20, 16, 10, 5),
    spent = c(685, 590, 392, 185, 123)
  ) |> 
  print()

# ==============================================================================

# LESSON: We pass a tibble through group_by to group it

sales

sales |> group_by(store) # note the display says "grouped"

# ==============================================================================

# USECASE: We can then summarize and get stats per group

sales |> 
  group_by(store) |> 
  summarize(
    customers = n_distinct(customer),
    items_sold = sum(items),
    total_sales = sum(spent),
    avg_items = mean(items),
    avg_spent = mean(spent)
  )

# ==============================================================================

# SETUP: Let's get a larger, more realistic dataset

# Extra pane > Packages tab > Install > nycflights13

library("nycflights13")

flights

# ==============================================================================

# USECASE: Find the carrier with the lowest average delays

flights |> 
  group_by(carrier) |> 
  summarize(m_delay = mean(dep_delay, na.rm = TRUE)) |> 
  arrange(m_delay)

# ==============================================================================

# LESSON: We can also group by multiple variables

# USECASE: Let's find the day of the year with the most flights

flights |> 
  group_by(month, day) |> 
  summarize(n_flights = n()) |> 
  arrange(desc(n_flights))

# ==============================================================================

# PITFALL: Note how this differs from grouping by just day

flights |> 
  group_by(day)|> 
  summarize(n_flights = n()) |> 
  arrange(desc(n_flights))

Pivot Longer and Wider

Both long and wide formats can be tidy
- Long formats are better for MLM
- Wide formats are better for SEM

It can be useful to quickly reshape a tibble
- pivot_longer(): wide → long
- pivot_wider(): long → wide

Pivot Longer Live Coding

# SETUP: We will need tidyverse and an example dataset (from workshop website)

library(tidyverse)

gradebook <- 
  read_csv("gradebook.csv") |> 
  print()

# ==============================================================================

# USECASE: We can pivot to long format by creating name and value variables

gradebook2 <- 
  gradebook |> 
  pivot_longer(
    cols = c(test1, test2, test3, test4, test5), 
    names_to = "test", 
    values_to = "grade"
  ) |> 
  print()

# ==============================================================================

# TIP: Use selection helpers to select columns quickly

gradebook2 <- 
  gradebook |> 
  pivot_longer(
    cols = test1:test5, 
    names_to = "test", 
    values_to = "grade"
  ) |> 
  print()

# ==============================================================================

# LESSON: Automatically remove the name prefix

gradebook2 <- 
  gradebook|> 
  pivot_longer(
    cols = starts_with("test"), 
    names_to = "test", 
    values_to = "grade",
    names_prefix = "test"
  ) |> 
  print()

Pivot Wider Live Coding

# SETUP: We will need tidyverse and an example dataset (from workshop website)

library(tidyverse)

diary <- read_csv("diary.csv") |> print()

# ==============================================================================

# USECASE: Reshape this long format to a wider format

diary_scale <- 
  diary |> 
  pivot_wider(
    names_from = "scale",
    values_from = "score"
  ) |> 
  print()

diary_day <- 
  diary |>  
  pivot_wider(
    names_from = "day",
    values_from = "score"
  ) |> 
  print()

# NOTE: There are thus multiple possible wide formats (for different uses)

# ==============================================================================

# LESSON: We can add a prefix to each name to avoid numeric names

diary_data

diary_day <- 
  diary |> 
  pivot_wider(
    names_from = "day",
    values_from = "score",
    names_prefix = "day_"
  ) |> 
  print()

# ==============================================================================

# LESSON: We can also pivot on multiple columns at once

diary_double <- 
  diary |> 
  pivot_wider(
    names_from = c("scale", "day"),
    values_from = "score"
  ) |> 
  print()

Visualize I

What is a graphic?

A data visualization expresses data through visual aesthetics.

Describing Graphics

Some simple graphics are easy to describe and may even have ready names.

Describing Graphics

A grammar of graphics will help us describe more complex graphics.

The Grammar of Graphics

The grammar of graphics is a set of rules for describing and creating data visualizations

To make our data visual (and therefore put our highly evolved occipital lobes to work)…
- We connect variables to visual qualities
- We represent observations as visual objects

This requires four fundamental elements
- We will first learn about them in lecture
- We will then apply them in R using {ggplot2}

Data

Graphics require data (e.g., tibbles), which describe observations using variables.

Aesthetic Mappings

Graphics require aesthetic mappings, which connect data variables to visual qualities.

Scales

Graphics require scales, which connect specific data values to specific aesthetic values.

Geometric Objects

Graphics require geometric objects (geoms), which represent the observations.

ggplot2 Basics

The ggplot2 package is a part of tidyverse
- No need to install or load it separately
- It plays nicely with tibbles and wrangling

It implements the grammar of graphics in R
- The “gg” stands for “grammar of graphics”
- Thus, we will need to provide all four elements

We will create a pseudo-pipeline of commands
- However, we will use + rather than |>
- This is because {ggplot2} predates the R pipe

ggplot2 Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

mpg

# ==============================================================================

# LESSON: First, set the data to a tibble
p <- ggplot(data = mpg)
p

# ==============================================================================

# LESSON: Next, set the aesthetic mappings with aes()

p <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy))
p

# ==============================================================================

# TIP: You can leave off the optional argument names

p <- ggplot(mpg, aes(x = displ, y = hwy))
p

# ==============================================================================

# LESSON: Next, set the positional scales

p <- ggplot(mpg, aes(x = displ, y = hwy)) +
  scale_x_continuous(
    name = "Engine Size (in liters)", 
    limits = c(1, 7), 
    breaks = 1:7
  ) +
  scale_y_continuous(
    name = "Highway Fuel Efficiency (in miles/gallon)",
    limits = c(10, 50),
    breaks = c(10, 20, 30, 40, 50)
  )
p

# ==============================================================================

# LESSON: Finally, add a point geom

p <- 
  ggplot(mpg, aes(x = displ, y = hwy)) + 
  scale_x_continuous(
    name = "Engine Size (in liters)", 
    limits = c(1, 7), 
    breaks = 1:7
  ) +
  scale_y_continuous(
    name = "Highway Fuel Efficiency (in miles/gallon)",
    limits = c(10, 50),
    breaks = c(10, 20, 30, 40, 50)
  ) +
  geom_point()

# ==============================================================================

# TIP: If you leave off the scales, R will try to guess

p <- ggplot(mpg, aes(x = displ, y = hwy)) + geom_point()
p

# ==============================================================================

# LESSON: We can also customize the geom with arguments

p <- ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point(color = "red", shape = "square", size = 2)
p

Basic Layering

ggplot2 uses a layered grammar of graphics
- We can keep stacking geoms on top

Layering adds a lot of possibilities
- We can convey more complex ideas
- We can learn more about our data

But we can still describe these graphics
- Just describe each layer in turn
- And describe the layers’ ordering

Basic Layering Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

mpg

# ==============================================================================

# USECASE: Add a smooth geom (i.e., line of best fit)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth()

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(method = "lm")

# ==============================================================================

# USECASE: Add a line geom (i.e., connecting points)

economics

ggplot(economics, aes(x = date, y = unemploy)) + 
  geom_point()

ggplot(economics, aes(x = date, y = unemploy)) + 
  geom_point() +
  geom_line(color = "orange", size = 1)

ggplot(economics, aes(x = date, y = unemploy)) + 
  geom_line(color = "orange", size = 1) +
  geom_point()

# ==============================================================================

# USECASE: Add reference line geoms

ggplot(economics, aes(x = date, y = unemploy)) + 
  geom_hline(yintercept = 0, color = "orange", size = 1) +
  geom_line(color = "blue", size = 1) +
  geom_point()

ggplot(economics, aes(x = date, y = unemploy)) + 
  geom_vline(xintercept = 7.5, color = "orange", size = 1) +
  geom_line(color = "blue", size = 1) +
  geom_point() 

ggplot(economics, aes(x = date, y = unemploy)) + 
  geom_abline(intercept = 4000, slope = 0.5, color = "orange", size = 1) +
  geom_line(color = "blue", size = 1) +
  geom_point()

Distribution Geoms

Variable distributions are critical in data analysis
- What are the most and least common values?
- What are the extrema (min and max values)?
- Are there any outliers or impossible values?
- How much spread is there in the variable?
- What shape does the distribution take?

Visualization is a quick way to assess this
- They can also communicate it to others

Distribution Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

mpg

# ==============================================================================

# USECASE: Creating histograms

ggplot(mpg, aes(x = hwy)) + 
  geom_histogram()

ggplot(mpg, aes(x = hwy)) + 
  geom_histogram(bins = 20)

ggplot(mpg, aes(x = hwy)) + 
  geom_histogram(binwidth = 2)

ggplot(mpg, aes(x = hwy)) + 
  geom_histogram(binwidth = 2, color = "red", size = 1)

ggplot(mpg, aes(x = hwy)) + 
  geom_histogram(binwidth = 2, color = "red", size = 1, fill = "white")

# ==============================================================================

# USECASE: Creating density plots

ggplot(mpg, aes(x = hwy)) + geom_density()

ggplot(mpg, aes(x = hwy)) + 
  geom_density(color = "red", size = 1, fill = "white")

# ==============================================================================

# USECASE: Creating box plots

ggplot(mpg, aes(x = hwy)) + geom_boxplot()

ggplot(mpg, aes(x = hwy, y = class)) + 
  geom_boxplot(varwidth = TRUE)

# ==============================================================================

# USECASE: Creating bar plots to count categorical variables

ggplot(mpg, aes(x = class)) + geom_bar()

# ==============================================================================

# PITFALL: Don't try to create histograms for categorical variables

ggplot(mpg, aes(x = class)) + geom_histogram() #error

Working with Color

Color scales come in two main types:
- Discrete scales have separate colors
  - Best with factor variables
- Continuous scales form a gradient
  - Best with numeric variables

There are two ways to control color:
- You can map color to a variable
  - It will take on different values
- You can set color to a value
  - It will take on one value only

Color Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

mpg

# ==============================================================================

# USECASE: Continuous color scales work well with numeric variables

ggplot(mpg, aes(x = hwy, y = cty, color = displ)) +
  geom_point(size = 4)

ggplot(mpg, aes(x = hwy, y = cty, color = displ)) +
  geom_point(size = 4) +
  scale_color_continuous(type = "viridis")

# ==============================================================================

# USECASE: Use a discrete color scale with categorical variables

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  scale_color_discrete(
    name = "Drivetrain", 
    breaks = c("4", "f", "r"), 
    labels = c("Four Wheel", "Front Wheel", "Rear Wheel")
  )

# ==============================================================================

# PITFALL: Don't forget to set categorical variables as factors

ggplot(mpg, aes(x = displ, y = hwy, color = cyl)) + 
  geom_point() # R guesses you want a continuous scale

ggplot(mpg, aes(x = displ, y = hwy, color = factor(cyl))) + 
  geom_point() + 
  scale_color_discrete(name = "Cylinders")

# ==============================================================================

# LESSON: Set a geom's color aesthetic to make it always that color

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(color = "red")

# ==============================================================================

# PITFALL: However, do this inside of geom() not aes()

ggplot(mpg, aes(x = displ, y = hwy, color = "blue")) + 
  geom_point() #unintended

# ==============================================================================

# LESSON: If you both set and map color, the setting will win

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) + 
  geom_point(color = "blue")

Other Aesthetics

For blocky elements like bars…
- color controls the outline color
- fill controls the internal color
- size controls the line thickness

Some mappings will induce grouping
- You’ll get separate geoms per group

It can be helpful to use redundant mapping
- Map one variable to multiple aesthetics
- Then if one “fails” the other may work

Other Aesthetics Live Coding

# SETUP: We will need tidyverse and an example dataset

library(tidyverse)

mpg

# ==============================================================================

# USECASE: Mapping the shape aesthetic to a categorical variable

ggplot(mpg, aes(x = displ, y = hwy, shape = drv)) +
  geom_point(size = 3)

# ==============================================================================

# PITFALL: Don't try to map shape to a continuous variable

ggplot(mpg, aes(x = displ, y = hwy, shape = hwy)) + 
  geom_point() #error

# NOTE: This doesn't work because there are way more numbers than shapes

# ==============================================================================

# LESSON: Color vs. Fill and Size for Blocks

ggplot(mpg, aes(y = class)) + 
  geom_bar()

ggplot(mpg, aes(y = class)) + 
  geom_bar(color = "darkred", fill = "lightblue", size = 1)

# ==============================================================================

# LESSON: Some aesthetics cause grouping when mapped to a categorical variable

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method = "lm") # single smooth

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(method = "lm") # three smooths

# ==============================================================================

# USECASE: Mapping to the fill aesthetic and setting the alpha property

ggplot(mpg, aes(x = hwy, fill = drv)) + 
  geom_density()

ggplot(mpg, aes(x = hwy, fill = drv)) + 
  geom_density(alpha = 0.3)

# ==============================================================================

# TIP: If you map the same variable to multiple aesthetics, you get redundancy

ggplot(mpg, aes(x = displ, y = hwy, shape = drv, color = drv)) +
  geom_point(size = 3) # if color fails, shape still works

Themes

Themes control how non-data elements look
- e.g., how thick to draw the gridlines
- e.g., where to position the legend

Complete themes change many elements at once
- Some are built into ggplot2
- Others come in R packages
- {papaja} provides theme_apa()

Individual elements can be customized too
- To do so, use theme() and this reference

Themes Live Coding

# SETUP: We will need tidyverse and an example graphic

library(tidyverse)

p <- 
  ggplot(mpg, aes(x = displ, y = hwy, color = drv)) + 
  geom_point() +
  labs(title = "Fuel Efficiency")
p

# ==============================================================================

# USECASE: Apply a "complete" theme

p + theme_bw()

p + theme_classic()

# ==============================================================================

# TIP: You can quickly change the font size of all elements with base_size

p + theme_grey(base_size = 24)

# ==============================================================================

# LESSON: The ggthemes package adds some fun complete themes

library(ggthemes)

p + theme_wsj()

p + theme_economist()

p + theme_stata()

# ==============================================================================

# LESSON: More more precise control, we can use theme()

p + theme(legend.position = "top")

p + theme(plot.title = element_text(color = "purple", face = "bold"))

p + theme(panel.grid = element_blank())

# NOTE: There are a lot of elements to learn, so use a cheatsheet!

Exporting Graphics

We may need to export graphics from R
- e.g., for a paper, poster, or presentation

This job is handling fantastically by ggsave()
- We can create many types of files
- We can customize the exact size

I recommend .png for most daily purposes
- For publishing, I prefer .pdf or .svg
- They retain perfect quality at any zoom
- You can send these files to most publishers

Exporting Live Coding

# SETUP: We will need tidyverse and an example graphic

library(tidyverse)

p <- ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth() +
  labs(x = "Engine Displacement", y = "Highway MPG")
p

# ==============================================================================

# USECASE: Save a specific ggplot object to a file

ggsave(filename = "pfinal.png", plot = p)

# ==============================================================================

# LESSON: Specify the size of the file to create

ggsave(filename = "pfinal2.png", plot = p, 
       width = 6, height = 3, units = "in")

# ==============================================================================

# LESSON: Just change the extension to create a different file type

ggsave(filename = "pfinal2.pdf", plot = p, 
       width = 6, height = 3, units = "in")

# ==============================================================================

# PITFALL: Creating a very large file may lead to small text

ggsave(filename = "p_poster.png", plot = p, 
       width = 12, height = 8, units = "in")

# ==============================================================================

# TIP: You can quickly increase the text size using base_size

p2 <- p + theme_grey(base_size = 24)

ggsave(filename = "p_poster2.png", plot = p2,
       width = 12, height = 8, units = "in")

Introduction to R for Social Scientists

Wrangle III

Summarize

Summarize Live Coding

Group Summarize

Group Summarize Live Coding

Pivot Longer and Wider

Pivot Longer Live Coding

Pivot Wider Live Coding

Visualize I

What is a graphic?

Describing Graphics

Describing Graphics

The Grammar of Graphics

Data

Aesthetic Mappings

Scales

Geometric Objects

ggplot2 Basics

ggplot2 Live Coding

Basic Layering

Basic Layering Live Coding

Distribution Geoms

Distribution Live Coding

Working with Color

Color Live Coding

Other Aesthetics

Other Aesthetics Live Coding

Themes

Themes Live Coding

Exporting Graphics

Exporting Live Coding

Practice IV

Introduction to R
for Social Scientists